Hurst exponent

Résumé

The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases. Studies involving the Hurst exponent were originally developed in hydrology for the practical matter of determining optimum dam sizing for the Nile river's volatile rain and drought conditions that had been observed over a long period of time. The name "Hurst exponent", or "Hurst coefficient", derives from Harold Edwin Hurst (1880–1978), who was the lead researcher in these studies; the use of the standard notation H for the coefficient also relates to his name. In fractal geometry, the generalized Hurst exponent has been denoted by H or Hq in honor of both Harold Edwin Hurst and Ludwig Otto Hölder (1859–1937) by Benoît Mandelbrot (1924–2010). H is directly related to fractal dimension, D, and is a measure of a data series' "mild" or "wild" randomness. The Hurst exponent i

Source officielle

https://en.wikipedia.org/wiki/Hurst_exponent

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (12)

Publications associées

Chargement

Personnes associées (2)

Personnes associées

Chargement

Unités associées (2)

Unités associées

Chargement

Concepts associés (2)

Long-range dependence (LRD), also called long memory or long-range persistence, is a phenomenon that may arise in the analysis of spatial or time series data. It relates to the rate of decay of statis

Le mouvement brownien fractionnaire (mBf) a été introduit par Kolmogorov en 1940, comme moyen d'engendrer des "spirales" gaussiennes dans des espaces de Hilbert. En 1968, Mandelbrot et Van Ness l'ont

Concepts associés

Chargement

Cours associés (3)

Cours associés

Chargement

Séances de cours associées (6)

Séances de cours associées

Chargement

Development of discrete surface growth models for frictional surfaces

Thomas Fry

Both natural and manufactured surfaces have been found to be self-affine over many length scales. Yet, it is still not understood what mechanisms lead to the final self-affine morphology, which is characterized by a persistent Hurst exponent (greater than 0.5, see next section 2.1 for the definitions). The scope of this project is then to develop new discrete growth models that can help gather insights in the formation of self-affine surfaces in frictional processes.

2020

Chargement

Accounting for inertia effects to access the high-frequency microrheology of viscoelastic fluids

Elena Bertseva, László Forró, Sylvia Jeney, Frank Scheffold

We study the Brownian motion of microbeads immersed in water and in a viscoelastic wormlike micelles solution by optical trapping interferometry and diffusing wave spectroscopy. Through the mean-square displacement obtained from both techniques, we deduce the mechanical properties of the fluids at high frequencies by explicitly accounting for inertia effects of the particle and the surrounding fluid at short time scales. For wormlike micelle solutions, we recover the 3/4 scaling exponent for the loss modulus over two decades in frequency as predicted by the theory for semiflexible polymers.

Amer Physical Soc2014

Chargement

, ,

We introduce stochastic models for flow fields with parameters that dictate the scale-dependent (self-similar) character of the field and control the balance between its rotational vs compressive behaviour. The development of our models is motivated by the availability of imaging modalities that measure flow vector fields (flow-sensitive MRI and Doppler ultrasound). To study such data, we formulate estimators of the model parameters, and use them to quantify the Hurst exponent and directional properties of synthetic and real-world flow fields (measured by means of phase-contrast MRI) in 3D.

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Chargement